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Rwanda sample PTI metadata input

rwa_mtdt_full.Rd

A named list mirroring the structure that [fct_template_reader()] produces from a PTI metadata Excel template. Bundled as the user-facing sample metadata input – pair with [rwa_shp] for a worked PTI calculation in `@examples` blocks across exported functions.

Usage

rwa_mtdt_full

Format

A named list of length 4:

general

`tbl_df` 1x1 – `country` ("Rwanda").

admin1_Province

`tbl_df` 5x7. Keys `admin0Pcod`, `admin1Pcod`, `admin1Name`, `year`; remaining 3 columns are the indicator values at province level (`poverty_rate`, `literacy_rate`, `road_density`).

admin2_District

`tbl_df` 30x8. Keys `admin0Pcod`, `admin1Pcod`, `admin2Pcod`, `admin2Name`, `year`; remaining 3 columns are the same indicators measured at district level.

metadata

`tbl_df` 3x14 – one row per indicator. Same 14-column schema as [ukr_mtdt_full]'s metadata sheet.

Note: this bundle does not include a `weights_clean` slot – [fct_template_reader()] only adds it when the source xlsx has a `weights_table` sheet, and the Rwanda template ships without one (deployers build weights interactively in the Shiny UI).

Source

Boundary keys (`adminNPcod`, `adminNName`) inherited from the geoBoundaries Rwanda data underpinning [rwa_shp] (CC-BY 4.0). Indicator values are synthetic, generated by the seeded script at `inst/template_pti/data-raw/generate-synthetic-metadata.R`.

Details

Compiled from `inst/template_pti/sample-data/sample-metadata-adm1-adm2.xlsx` (the multi-level synthetic Rwanda workbook): 3 indicators measured at admin2_District (with admin1_Province also populated for the Data Explorer tab).

Compared with [ukr_mtdt_full]: smaller (3 indicators vs 9), simpler (2 admin levels vs 4), and synthetic-by-design (no real social or economic data) – safe to publish in tutorials and screenshots.

See also

Other sample-data: rwa_shp, ukr_mtdt_full, ukr_shp

Examples

data(rwa_mtdt_full)
names(rwa_mtdt_full)
#> [1] "general"         "admin1_Province" "admin2_District" "metadata"       
rwa_mtdt_full$general
#> # A tibble: 1 × 1
#>   country
#>   <chr>  
#> 1 Rwanda 
head(rwa_mtdt_full$metadata)
#> # A tibble: 3 × 14
#>   var_code      var_name       var_description var_order var_units spatial_level
#>   <chr>         <chr>          <chr>               <dbl> <chr>     <chr>        
#> 1 poverty_rate  Poverty rate   Share of popul…         1 %         admin2_Distr…
#> 2 literacy_rate Adult literac… Share of adult…         2 %         admin2_Distr…
#> 3 road_density  Road density   Length of pave…         3 km/km^2   admin2_Distr…
#> # ℹ 8 more variables: pillar_group <chr>, pillar_name <chr>,
#> #   pillar_description <chr>, fltr_exclude_pti <lgl>,
#> #   fltr_exclude_explorer <lgl>, fltr_overlay_pti <lgl>,
#> #   fltr_overlay_explorer <lgl>, legend_revert_colours <lgl>